We propose a framework for energy-based human activity recognition in a household environment. We apply machine learning techniques to infer the state of household appliances from their energy consumption data and use rule- based scenarios that exploit these states to detect human activity. Our decision engine achieved a 99.1% accuracy for real-world data collected in the kitchens of two smart homes.
|Conference||2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)|
|Period||19/03/18 → 23/03/18|